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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3233 条记录，以下是561-570 订阅

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Detection of Sensors Fault Using Singular Value Decomposition Technique in Power Plant

Detection of Sensors Fault Using Singular Value Decompositio...

引用

2023 international conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Helen, R. Nikei Antony Miekle, A. Thenmozhi, T. Suhail Ahmed, A. Aravindh, R. Saveetha Engineering College Department of Medical Electronics Chennai India Thiagarajar College of Engineering Eee Department Madurai India

ISBN: (纸本)9798350300857

Temperature sensors are critical components in power plants, and their accurate operation is essential for efficient plant performance and safety. However, temperature sensors are susceptible to various faults such as offset, and sensor failure, which can lead to incorrect temperature readings and affect the overall plant operation. Therefore, it is crucial to detect and diagnose temperature sensor faults to secure the safety and dependability of the plant. Integrating machine learning methods into existing sensor systems for fault detection poses significant challenges. Seamless integration with data acquisition systems and sensor networks is essential but can be complex due to differences in data formats and communication protocols. Real-time processing requirements may necessitate optimization for low latency. Ensuring that machine learning models run efficiently on embedded, or edge devices can also be challenging. Furthermore, integrating the output of machine learning models into decision-making processes or control systems requires careful consideration to prevent false alarms or missed faults. This study proposes the implementation of Singular Value Decomposition (SVD) for detecting sensor faults using Lab *** method utilizes SVD to extract feature vectors from sensor data, which are then compared to reference vectors to identify faulty sensors. The Lab Viewplatform is used to develop the software application for processing the sensor data and performing the SVD analysis. The proposed method offers a reliable and efficient approach to detecting sensor faults in industrial systems, potentially improving system reliability and reducing downtime. © 2023 IEEE.

关键词： Singular value decomposition

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ICCECE 2023 - international conference on Computer, Electrical and Communication engineering

ICCECE 2023 - International Conference on Computer, Electric...

引用

2023 international conference on Computer, Electrical and Communication engineering, ICCECE 2023

ISBN: (纸本)9781665452519

The proceedings contain 44 papers. The topics discussed include: human interaction-free object localization in a scene;ensemble learning and its application in spam detection;a review of multi-band reflectarray antenna designs with mutual coupling considerations;design and development of cost-effective automatic solar panel cleaning system;model based test framework for verification of flight control software;a fast-converging radial basis function neural network-based MPPT controller for static and dynamic variations in solar irradiation;diabetic retinopathy - an ensemble approach;a microcontroller based fir filter with dynamic stabilization of sampling frequency;analyzing and addressing data-driven fairness issues in machine learning models used for societal problems;envy prediction from users’ photos using convolutional neural networks;and predicting gender from human or non-human social media profile photos by using transfer learning.

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Gray-box Adversarial Attack of Deep Reinforcement learning-based Trading Agents 22

Gray-box Adversarial Attack of Deep Reinforcement Learning-b...

引用

22nd IEEE international conference on machine learning and Applications, ICMLA 2023

作者： Ataiefard, Foozhan Hemmati, Hadi University of Calgary Electrical and Software Engineering Calgary Canada York University Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350345346

In recent years, deep reinforcement learning (Deep RL) has been successfully implemented as a smart agent in many systems such as complex games, self-driving cars, and chat-bots. One of the interesting use cases of Deep RL is its application as an automated stock trading agent. In general, any automated trading agent is prone to manipulations by adversaries in the trading environment. Thus studying their robustness is vital for their success in practice. However, typical mechanism to study RL robustness, which is based on white-box gradient-based adversarial sample generation techniques (like FGSM), is obsolete for this use case, since the models are protected behind secure international exchange APIs, such as NASDAQ. In this research, we demonstrate that a 'gray-box' approach for attacking a Deep RL-based trading agent is possible by trading in the same stock market, with no extra access to the trading agent. In our proposed approach, an adversary agent uses a hybrid Deep Neural Network as its policy consisting of Convolutional layers and fully-connected layers. On average, over three simulated trading market configurations, the adversary policy proposed in this research is able to reduce the reward values by 214.17%, which results in reducing the potential profits of the baseline by 139.4%, ensemble method by 93.7%, and an automated trading software developed by our industrial partner by 85.5%, while consuming significantly less budget than the victims (427.77%, 187.16%, and 66.97%, respectively). © 2023 IEEE.

关键词： Adversarial At-tacks Automated Trading Deep Reinforcement learning Robustness

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Gender Classification of Social Network Text Using Natural Language Processing and machine learning Approaches 20

Gender Classification of Social Network Text Using Natural L...

引用

20th international Joint conference on Computer Science and software engineering, JCSSE 2023

作者： Jintawatsakoon, Supanat Poonsawat, Ekkapob College of Digital Innovation Technology Rangsit University Pathum Thani Thailand True Digital Group Co. Ltd. Department of Data Analytics Bangkok Thailand

ISBN: (纸本)9798350300505

Gender is a crucial consideration in many fields of study. In the era of social networks, massive volumes of data are gathered and processed, enabling us to use this data for a variety of purposes. Our objective to build a gender classification model based on Thai text using natural language processing (NLP) and a machine learning approach. We collected the data on social media websites using web scraping. TF-IDF and n-gram were applied for feature extraction tasks. Logistic Regression, Naïve Bayes, and Random Forest have implemented classification models. Accuracy, precision, recall, and f1 score are used as evaluation metrics and demonstrate that the Logistic Regression model trained on the features derived from data received from texts longer than 200 words produces the best outcome. The dataset is available at https://***/supanat/***. © 2023 IEEE.

关键词： Logistic regression

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Anomaly Detection in Dynamic Graphs Via Long Short-Term Temporal Attention Network

Anomaly Detection in Dynamic Graphs Via Long Short-Term Temp...

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2023 international conference on machine learning and Cybernetics, ICMLC 2023

作者： Gao, Zu-Kang Ma, Le-Ming Li, Mei-Ting Zhao, Gen Li, Mark Jun-Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350303780

Anomaly detection in dynamic graphs has drawn increasing attention in social networks, e-commerce, and cybersecurity etc. Capturing the evolution patterns using temporal features is crucial for dynamic graphs anomaly detection. However, existing approaches do not thoroughly consider the long-term and short-term temporal features, which lead to the loss of information about the evolution patterns of dynamic graphs. In this paper, we propose a end-to-end an anomaly detection algorithm in dynamic graphs via Long Short-term Temporal Attention network (GraphLSTA). GraphLSTA captures the evolution patterns of dynamic graphs by effectively extracting and fusing the long-term and short-term temporal features through a recurrent attention mechanism, improving the detection performance. © 2023 IEEE.

关键词： Anomaly detection

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A Comparative Study on Deep Networks for Glaucoma Classification

A Comparative Study on Deep Networks for Glaucoma Classifica...

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2023 international conference on machine learning and Automation, conf-MLA 2023

作者： Ying, Zifan Wang, Zhichong Zhang, Hongbo Zhang, Rongxuan ZJU-UIUC Institute ZJUI Zhejiang University Hangzhou China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China Department of Computer Science Technology BNU-HKBU United International College Zhuhai China Department of Electrical Engineering University of California Davis DavisCA United States

The purpose of this study is to classify glaucoma and non-glaucoma images from REFUGE dataset of fundus images. Due to the imbalance of dataset, we did data augmentation and preprocessing for dataset first (including feature extraction and enhancement). We then tested the performance of some deep convolutional neural networks as baselines, including ResNet, GoogLeNet, and VGGNet. Later we introduced self-attention layer into our CNN model and tried a method based on cup-to-disc ratio. Compared to the unprocessed dataset, the processed (data augmentation and feature enhancement) dataset gave a better performace. And self-attention model also improved performance beyond original CNN. Finally our method base on the cup-to-disc ratio was way better than the CNN models above. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Classification (of information)

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Proposed Framework for Performance Tuning in Enterprise Applications using machine learning 7

Proposed Framework for Performance Tuning in Enterprise Appl...

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7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Khosla, Cherry Saini, Baljit Singh School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798350340600

IT systems and enterprise applications are crucial and their failure can lead to production losses, data loss, and missed commercial opportunities, resulting in costly efforts to recover. Nowadays, most of the applications are data-intensive, as a result of which the systems and the applications are becoming more and more complex in terms of their configurations. There are hundreds of configuration knobs that affect the performance of the system. Due to these changing environments and requirements of the software systems, the systems should be robust, versatile, autonomous, energy efficient and customizable at run-time. These requirements of the system to maintain its performance has surpassed human capabilities. Therefore, this research study proposes a novel approach to automatically tune the performance of the system using machine learning approaches. Here, the idea is to propose a technique that will identify anomalous behavior of the system because of one or more configurational parameters and auto-remediate the system to its normal state. © 2023 IEEE.

关键词： Anomaly detection

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machine learning Based Sentiment Analysis of Tweets

Machine Learning Based Sentiment Analysis of Tweets

引用

2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Khatter, Harsh Aggrawal, Niyati Shrivastava, Ajay Kumar Kiet Group of Institutions Department of Computer Science Delhi-NCR Ghaziabad India Jaypee Institute of Information Technology Department of Computer Science & Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350344387

Sentiment Analysis is one of the computer science study areas that is expanding quickly, making it difficult to keep up with all the activity to fit in the business *** Analysis is used in fields of Stock Markets, Social sites like Twitter, Instagram, Facebook, elections, disasters, medicine, Sentiment Analysis in Arabic languages, software engineering to analyse the products based on the polarity scores that are classified in Positive Sentiments, Negative Sentiments and Neutral Sentiments. Here we also analysed the Sentiments of peoples outside the eco places such as park are more positive than peoples inside the park. We here used NLTK, VADER module, some deep learning modules like Roberta, Text blob and a Tweepy API. Based on the dataset utilized, the domain covered, the Arabic language type, the pre-processing techniques, the features used, the word representation, the methodology employed, and the evaluation metrics used to evaluate the suggested techniques, the included research were examined. Using the input given by Twitter popups and characterizing impressions, we present an economic analysis and responses to the wildfires that occurred in Portugal and Spain. We employ methods for machine learning technique to find the info in this text. We calculate a value for the relationship between attitudes toward wildfires and air quality and exposure are determined by Euclidean distance from the catastrophic event. © 2023 IEEE.

关键词： Information Retrieval machine learning NLTK Sentiment Analysis Text blob Twitter Tweets

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ISE 2023 - Proceedings of the 2nd international Workshop on Intelligent software engineering, co-located with the 30th Asia-Pacific software engineering conference, APSEC 2023

ISE 2023 - Proceedings of the 2nd International Workshop on ...

引用

2nd international Workshop on Intelligent software engineering, ISE 2023

The proceedings contain 11 papers. The topics discussed include: topic-aware video summarization technique for product reviews exploiting the BERTopic and BART models;GAN-based image generation techniques exploiting latent vector distribution and edge loss methods on limited datasets;a combination of classification and summarization techniques for bug report summarization;developing machine learning systems: situational factors shaping the development process;fault localization with DNN-based test case learning and ablated execution traces;measuring modularity in JavaScript-based microservices from software architectural perspectives;a survey of software health management in unmanned aerial vehicle systems;hyperparameter optimization technique in autonomous racing cars exploiting the leaky piece and conquer fireworks algorithm;and a survey of health management system for on-the-fly repairing of concurrency errors in airborne software.

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Quantum Many-Body Problems: Quantum machine learning Applications 1

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international conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Medisetty, Pramoda Vulavalapudi, Veda Manohara Sunanda Evuru, Poorna Chand Mahmud, Mufti Prakash, Kolla Bhanu Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computing and Technology Nottingham Trent University Nottingham United Kingdom

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

A groundbreaking approach has been developed to accurately depict and predict the essential traits of quantum many-body systems. This inventive technique merges quantum ground state computations, classical shadow representations, and sophisticated machine learning approaches. The newly engineered software precisely identifies the ground state of a 2D antiferromagnetic Heisenberg model and forms a classical shadow representation for the quantum state. machine learning models are deployed to forecast ground-state correlation functions with remarkable accuracy. This pioneering technique shows enormous potential in transforming quantum simulations and quantum machine learning, offering flexible solutions for complex quantum *** implications extend to fields such as material science and drug discovery. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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